Article · Computación y Sistemas · March 31, 2026
Revisiting Arabic Morphology: A Machine-Learning-Based Approach to Stemming and Root Character Permutations with a Publicly Available Open-Source Implementation
Iskander Akhmetov, Basem Ibrahim Malawi Al-Raba’a, Alexander F. Gelbukh, Rustam Mussabayev, Alexander Krassovitskiy
Abstract
We present a novel method for learning morphological rules from a corpus for a major language with very rich and complicated morphology. Namely, we conducted experiments on Arabic stemming and root character permutations, focusing on their properties and semantic relations. First, we built stemmer and lemmatizer models using an only 0.2713/0.6347 accuracy score for the test/train sets. Second, we have explored the semantic relationship between word root character permutation variants. We have found that recombining the characters in a root may give rise to antonymy, synonymy, or other semantic relations.
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